Data Science and Big Data Analytics in Financial Services: A Case Study

Data Science and Big Data Analytics in Financial Services: A Case Study

Suren Behari (University of Southern Queensland, Australia), Aileen Cater-Steel (University of Southern Queensland, Australia) and Jeffrey Soar (University of Southern Queensland, Australia)
DOI: 10.4018/978-1-5225-0135-0.ch017
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The chapter discusses how Financial Services organizations can take advantage of Big Data analysis for disruptive innovation through examination of a case study in the financial services industry. Popular tools for Big Data Analysis are discussed and the challenges of big data are explored as well as how these challenges can be met. The work of Hayes-Roth in Valued Information at the Right Time (VIRT) and how it applies to the case study is examined. Boyd's model of Observe, Orient, Decide, and Act (OODA) is explained in relation to disruptive innovation in financial services. Future trends in big data analysis in the financial services domain are explored.
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Foreign exchange markets generate large volumes of data. Some of the information hidden within these interactions could be valuable to market participants. In the past there were limitations on accessing some of this data. In recent years analytics technology has developed to the point where companies are able to extract insights from such data.

Financial organizations may need to use streaming analytics (which operate in real time) in conjunction with historical data from conventional business intelligence systems if they are to improve retention and identify potential clients, both retail and institutional. This integration allows for customer profiling, not just to personalize offers but to achieve more optimal timing. In foreign exchange markets, where an institution wants to check thousands of transactions per second, streaming analytics can enable it to narrow the focus and monitor the success rate in quoting foreign exchange prices to potential customers.

Historical analysis can reveal a particular customer's usual transaction size, timing and individual price point relative to a median in the market. Dynamic pricing that ensures a profit on a specific transaction at a specific time, while meeting the customer's likely requirements based on their profile, requires the use of real-time analysis.

The use of streamed real-time data is becoming increasingly common. In the development and marketing of foreign exchange products, the typical foundational capabilities we might see include predictive customer intelligence and big-data analytics, coupled with enterprise marketing management.

Banks already have large quantities of real-time data that they analyze for very specific needs as dictated by their current analytical capabilities, which have been designed to allow the users to carry out broadly predetermined analyses.

Deutsche Bank for example uses a big-data application called FiREapps to aggregate, validate and analyze underlying exposure data from corporate customers. Its FX trade execution services customers then use the results of this analysis to manage their overall currency exposures and ensure their trades are compliant with corporate foreign exchange policy.

Key Terms in this Chapter

Valued Information at the Right Time (VIRT): An approach that employs dynamic context and operator requirements to assure that high-value information flows quickly where it’s needed and is processed promptly by recipients.

Big Data: A broad term for large and complex data sets that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from data, and seldom to a particular size of data set.

Data Science: The extraction of knowledge from large volumes of unstructured data which is a continuation of the field data mining and predictive analytics, also known as knowledge discovery and data mining (KDD).

OODA Loops (OODA): Refers to the decision cycle of observe, orient, decide, and act, developed by military strategist and USAF Colonel John Boyd. Boyd applied the concept to the combat operations process, often at the strategic level in military operations. It is now also often applied to understand commercial operations and learning processes.

Analytics-as-a-Service (AaaS): An approach to an extensible platform that can provide cloud-based analytical capabilities over a variety of industries and use cases, that covers the end-to-end capabilities of an analytical solution, from data acquisition to end-user visualization, reporting and interaction.

Cloud Computing: A general term for anything that involves delivering hosted services over the Internet. These services are broadly divided into: Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS), and Analytics-as-a-Service (AaaS).

Disruptive Innovation (DI): A process where a product or service that initially enters a new market or starts at the bottom of an existing market, and then moves up market displacing existing competition.

Platform-as-a-Service (PaaS): A category of cloud computing that is a proven model for building and running applications and services over the Internet without the hassle of maintaining the hardware and software infrastructure at your company.

Amazon Web Services (AWS): A flexible, cost-effective, easy-to-use cloud computing platform that delivers a comprehensive portfolio of secure and scalable cloud computing services in a self-service, pay-as-you-go model, with zero capital expense needed to handle your big data analytics workloads, such as real-time streaming analytics, data warehousing, NoSQL and relational databases, object storage, analytics tools, and data workflow services.

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